Data Scientist

Responsibilities:

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  Model Development & Implementatio n: Design, build, and deploy machine learning models to solve complex business problems. These may include predictive analytics, recommendation systems, anomaly detection, classification, and other machine learning tasks
• .AI Solution s: Create and optimize AI-driven solutions that can automate processes, improve decision-making, and enhance user experiences across various business functions
• .Data Analysis & Exploratio n: Conduct thorough analysis of large datasets to uncover patterns, trends, and insights that inform business strategies and model development
• .Collaborative Problem Solvin g: Partner with business stakeholders to understand their goals and challenges, and translate these into data-driven solutions and models that align with the organization’s objectives
• .Model Optimizatio n: Continuously monitor, fine-tune, and improve the performance of machine learning models through evaluation, experimentation, and iteration
• .Scalability & Performanc e: Ensure that AI models and solutions can scale effectively in production environments, handling large volumes of data while maintaining high performance and reliability
• .Stay Current with AI Trend s: Keep up-to-date with the latest advancements in AI, machine learning, and data science to propose innovative techniques and technologies that can drive competitive advantage
• .Reporting & Communicatio n: Present findings, model results, and actionable insights to both technical and non-technical stakeholders, ensuring clarity and alignment on business impacts

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Requirements

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  Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related fie
• ld.Proven experience (5+ years) as a Data Scientist, with a focus on machine learning, AI, and advanced analyti
• cs.Strong communication skills, with the ability to explain complex technical concepts to non-technical stakeholde
• rs.Strong proficiency in programming languages such as Python, R, or simil
• ar.Hands-on experience with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn, Keras) and tools for model development, training, and evaluati
• on.Expertise in data manipulation and analysis using tools like Pandas, NumPy, SQL, or simil
• ar.Solid understanding of statistical methods, hypothesis testing, and data modeling techniqu
• es.Experience with cloud platforms (e.g., AWS, Azure, GCP) and deploying models in cloud environments is a pl

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